Overview

Dataset statistics

Number of variables18
Number of observations5000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory703.2 KiB
Average record size in memory144.0 B

Variable types

NUM17
CAT1

Warnings

T(C) has constant value "5000" Constant
Vol(aq) is highly correlated with b(H2O)High correlation
b(H2O) is highly correlated with Vol(aq)High correlation
nCa(s) is highly correlated with b(CaO)High correlation
b(CaO) is highly correlated with nCa(s)High correlation
nSi(s_reac) is highly correlated with b(SiO2)High correlation
b(SiO2) is highly correlated with nSi(s_reac)High correlation
nCa(CSHQ) is highly correlated with mCSHQ and 2 other fieldsHigh correlation
mCSHQ is highly correlated with nCa(CSHQ) and 3 other fieldsHigh correlation
nSi(CSHQ) is highly correlated with mCSHQHigh correlation
nH2O(CSHQ) is highly correlated with mCSHQ and 2 other fieldsHigh correlation
C/S(CSHQ) is highly correlated with pHHigh correlation
pH is highly correlated with C/S(CSHQ)High correlation
nGelPW(CSH) is highly correlated with mCSHQ and 2 other fieldsHigh correlation
nCa(s) is uniformly distributed Uniform
b(CaO) has unique values Unique
b(SiO2) has unique values Unique
b(H2O) has unique values Unique
nSi(aq) has unique values Unique
nSi(s_reac) has unique values Unique
nPortlandite has 1872 (37.4%) zeros Zeros
nAmor-Sl has 4414 (88.3%) zeros Zeros

Reproduction

Analysis started2022-11-01 15:13:49.427343
Analysis finished2022-11-01 15:15:24.616513
Duration1 minute and 35.19 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

T(C)
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.1 KiB
25
5000 
ValueCountFrequency (%) 
255000100.0%
 
2022-11-01T10:15:24.851629image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-11-01T10:15:25.007891image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:15:25.159577image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

b(CaO)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct5000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9500005856
Minimum0.1002878
Maximum1.799994
Zeros0
Zeros (%)0.0%
Memory size39.1 KiB
2022-11-01T10:15:25.456467image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.1002878
5-th percentile0.18512509
Q10.525001725
median0.94990835
Q31.3748415
95-th percentile1.71475115
Maximum1.799994
Range1.6997062
Interquartile range (IQR)0.849839775

Descriptive statistics

Standard deviation0.4907955093
Coefficient of variation (CV)0.5166265334
Kurtosis-1.199992227
Mean0.9500005856
Median Absolute Deviation (MAD)0.4249746
Skewness7.156988582e-06
Sum4750.002928
Variance0.2408802319
MonotocityNot monotonic
2022-11-01T10:15:25.775774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.50806031< 0.1%
 
0.2413631< 0.1%
 
0.42597641< 0.1%
 
1.4781511< 0.1%
 
1.5136231< 0.1%
 
0.2760011< 0.1%
 
0.98996341< 0.1%
 
1.3660351< 0.1%
 
0.29305971< 0.1%
 
1.079241< 0.1%
 
Other values (4990)499099.8%
 
ValueCountFrequency (%) 
0.10028781< 0.1%
 
0.10053221< 0.1%
 
0.10078361< 0.1%
 
0.10108791< 0.1%
 
0.1014971< 0.1%
 
ValueCountFrequency (%) 
1.7999941< 0.1%
 
1.7995231< 0.1%
 
1.7991851< 0.1%
 
1.7989451< 0.1%
 
1.7983581< 0.1%
 

b(SiO2)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct5000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4499995845
Minimum0.2000555
Maximum0.699901
Zeros0
Zeros (%)0.0%
Memory size39.1 KiB
2022-11-01T10:15:26.111099image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.2000555
5-th percentile0.22502607
Q10.325049325
median0.449969
Q30.57494485
95-th percentile0.674954545
Maximum0.699901
Range0.4998455
Interquartile range (IQR)0.249895525

Descriptive statistics

Standard deviation0.1443523193
Coefficient of variation (CV)0.320783228
Kurtosis-1.200006387
Mean0.4499995845
Median Absolute Deviation (MAD)0.1249792
Skewness4.178746263e-06
Sum2249.997923
Variance0.0208375921
MonotocityNot monotonic
2022-11-01T10:15:26.444750image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.30408131< 0.1%
 
0.65026061< 0.1%
 
0.44336831< 0.1%
 
0.4214441< 0.1%
 
0.49122971< 0.1%
 
0.53932371< 0.1%
 
0.29503271< 0.1%
 
0.67315421< 0.1%
 
0.35350131< 0.1%
 
0.6873771< 0.1%
 
Other values (4990)499099.8%
 
ValueCountFrequency (%) 
0.20005551< 0.1%
 
0.20019981< 0.1%
 
0.20022611< 0.1%
 
0.20038491< 0.1%
 
0.20042131< 0.1%
 
ValueCountFrequency (%) 
0.6999011< 0.1%
 
0.69989581< 0.1%
 
0.69972811< 0.1%
 
0.69968341< 0.1%
 
0.6995281< 0.1%
 

b(H2O)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct5000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.550848675
Minimum2.77561
Maximum8.325583
Zeros0
Zeros (%)0.0%
Memory size39.1 KiB
2022-11-01T10:15:26.819750image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2.77561
5-th percentile3.0535027
Q14.163307
median5.550589
Q36.93849475
95-th percentile8.0487441
Maximum8.325583
Range5.549973
Interquartile range (IQR)2.77518775

Descriptive statistics

Standard deviation1.602547853
Coefficient of variation (CV)0.2887032141
Kurtosis-1.200010962
Mean5.550848675
Median Absolute Deviation (MAD)1.3877115
Skewness-5.04983298e-07
Sum27754.24338
Variance2.568159622
MonotocityNot monotonic
2022-11-01T10:15:27.128318image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3.4388761< 0.1%
 
6.3945221< 0.1%
 
4.3214081< 0.1%
 
6.1531271< 0.1%
 
6.2989581< 0.1%
 
4.8874171< 0.1%
 
5.5356761< 0.1%
 
7.1548821< 0.1%
 
5.0896361< 0.1%
 
5.4132161< 0.1%
 
Other values (4990)499099.8%
 
ValueCountFrequency (%) 
2.775611< 0.1%
 
2.7769151< 0.1%
 
2.778211< 0.1%
 
2.7794271< 0.1%
 
2.7807091< 0.1%
 
ValueCountFrequency (%) 
8.3255831< 0.1%
 
8.32481< 0.1%
 
8.3236731< 0.1%
 
8.3228071< 0.1%
 
8.3216691< 0.1%
 

Vol(aq)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct4998
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07414344758
Minimum0.00616381
Maximum0.1440867
Zeros0
Zeros (%)0.0%
Memory size39.1 KiB
2022-11-01T10:15:28.227238image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.00616381
5-th percentile0.025499436
Q10.0490410625
median0.07407904
Q30.0993776875
95-th percentile0.12303019
Maximum0.1440867
Range0.13792289
Interquartile range (IQR)0.050336625

Descriptive statistics

Standard deviation0.03084359636
Coefficient of variation (CV)0.4159989502
Kurtosis-0.9364413312
Mean0.07414344758
Median Absolute Deviation (MAD)0.025199435
Skewness0.0133111224
Sum370.7172379
Variance0.0009513274361
MonotocityNot monotonic
2022-11-01T10:15:28.586605image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.10746272< 0.1%
 
0.1157452< 0.1%
 
0.046061491< 0.1%
 
0.055232141< 0.1%
 
0.023188391< 0.1%
 
0.06234431< 0.1%
 
0.074910151< 0.1%
 
0.075326541< 0.1%
 
0.077104761< 0.1%
 
0.075431771< 0.1%
 
Other values (4988)498899.8%
 
ValueCountFrequency (%) 
0.006163811< 0.1%
 
0.0061705941< 0.1%
 
0.0070321551< 0.1%
 
0.00807871< 0.1%
 
0.0080914271< 0.1%
 
ValueCountFrequency (%) 
0.14408671< 0.1%
 
0.14392721< 0.1%
 
0.14314011< 0.1%
 
0.14309831< 0.1%
 
0.14266741< 0.1%
 

pH
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1297
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.9775954
Minimum9.70932
Maximum12.47262
Zeros0
Zeros (%)0.0%
Memory size39.1 KiB
2022-11-01T10:15:28.914747image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum9.70932
5-th percentile9.793655
Q111.8411225
median12.47261
Q312.47261
95-th percentile12.47261
Maximum12.47262
Range2.7633
Interquartile range (IQR)0.6314875

Descriptive statistics

Standard deviation0.8869026982
Coefficient of variation (CV)0.07404680729
Kurtosis1.53780882
Mean11.9775954
Median Absolute Deviation (MAD)0
Skewness-1.742465985
Sum59887.97699
Variance0.7865963961
MonotocityNot monotonic
2022-11-01T10:15:29.194652image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
12.47261311962.4%
 
9.7936553497.0%
 
9.793657681.4%
 
9.793656661.3%
 
9.793658390.8%
 
9.793654310.6%
 
9.793653120.2%
 
9.793659100.2%
 
12.4726250.1%
 
9.79365250.1%
 
Other values (1287)129625.9%
 
ValueCountFrequency (%) 
9.709321< 0.1%
 
9.793651< 0.1%
 
9.79365140.1%
 
9.79365250.1%
 
9.793653120.2%
 
ValueCountFrequency (%) 
12.4726250.1%
 
12.47261311962.4%
 
12.47262< 0.1%
 
12.472591< 0.1%
 
12.471961< 0.1%
 

nCa(aq)
Real number (ℝ≥0)

Distinct4998
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0009993655666
Minimum3.227392e-05
Maximum0.002708732
Zeros0
Zeros (%)0.0%
Memory size39.1 KiB
2022-11-01T10:15:29.507169image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3.227392e-05
5-th percentile7.1388688e-05
Q10.000289257
median0.00091292075
Q30.00161603325
95-th percentile0.00225606105
Maximum0.002708732
Range0.00267645808
Interquartile range (IQR)0.00132677625

Descriptive statistics

Standard deviation0.000737743178
Coefficient of variation (CV)0.7382115241
Kurtosis-1.139941692
Mean0.0009993655666
Median Absolute Deviation (MAD)0.0006533
Skewness0.3369908685
Sum4.996827833
Variance5.442649967e-07
MonotocityNot monotonic
2022-11-01T10:15:29.850914image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.0013004172< 0.1%
 
0.00026942142< 0.1%
 
0.00093360081< 0.1%
 
0.0022826891< 0.1%
 
0.00013464431< 0.1%
 
0.0015183141< 0.1%
 
0.0015267571< 0.1%
 
8.090159e-051< 0.1%
 
0.0015288881< 0.1%
 
0.0018107891< 0.1%
 
Other values (4988)498899.8%
 
ValueCountFrequency (%) 
3.227392e-051< 0.1%
 
3.354156e-051< 0.1%
 
3.355427e-051< 0.1%
 
3.560395e-051< 0.1%
 
3.707615e-051< 0.1%
 
ValueCountFrequency (%) 
0.0027087321< 0.1%
 
0.0026768061< 0.1%
 
0.0026703941< 0.1%
 
0.0026665421< 0.1%
 
0.0026585621< 0.1%
 

nCa(s)
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM

Distinct4996
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9490012225
Minimum0.1001453
Maximum1.79886
Zeros0
Zeros (%)0.0%
Memory size39.1 KiB
2022-11-01T10:15:30.179779image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.1001453
5-th percentile0.185002265
Q10.524107175
median0.9485624
Q31.37332175
95-th percentile1.71374705
Maximum1.79886
Range1.6987147
Interquartile range (IQR)0.849214575

Descriptive statistics

Standard deviation0.4904275505
Coefficient of variation (CV)0.5167828438
Kurtosis-1.199874503
Mean0.9490012225
Median Absolute Deviation (MAD)0.42466755
Skewness0.001352342864
Sum4745.006112
Variance0.2405191823
MonotocityNot monotonic
2022-11-01T10:15:30.492279image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.3939572< 0.1%
 
1.543052< 0.1%
 
1.0913792< 0.1%
 
1.2852532< 0.1%
 
0.50712671< 0.1%
 
0.2412521< 0.1%
 
1.5120961< 0.1%
 
0.27592011< 0.1%
 
0.98843451< 0.1%
 
1.3642241< 0.1%
 
Other values (4986)498699.7%
 
ValueCountFrequency (%) 
0.10014531< 0.1%
 
0.1003951< 0.1%
 
0.10068321< 0.1%
 
0.10103091< 0.1%
 
0.10141081< 0.1%
 
ValueCountFrequency (%) 
1.798861< 0.1%
 
1.7982041< 0.1%
 
1.7977571< 0.1%
 
1.7971521< 0.1%
 
1.7968561< 0.1%
 

nSi(aq)
Real number (ℝ≥0)

UNIQUE

Distinct5000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.044689371e-05
Minimum1.90563e-07
Maximum0.000567797
Zeros0
Zeros (%)0.0%
Memory size39.1 KiB
2022-11-01T10:15:30.820399image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1.90563e-07
5-th percentile7.97064605e-07
Q11.76471575e-06
median2.91812e-06
Q31.33703e-05
95-th percentile0.000399605965
Maximum0.000567797
Range0.000567606437
Interquartile range (IQR)1.160558425e-05

Descriptive statistics

Standard deviation0.0001225131868
Coefficient of variation (CV)2.428557595
Kurtosis6.410266314
Mean5.044689371e-05
Median Absolute Deviation (MAD)1.4992935e-06
Skewness2.746721691
Sum0.2522344685
Variance1.500948095e-08
MonotocityNot monotonic
2022-11-01T10:15:31.132972image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.424097e-061< 0.1%
 
0.00041667841< 0.1%
 
2.071284e-051< 0.1%
 
2.315898e-061< 0.1%
 
2.328828e-061< 0.1%
 
0.00030379651< 0.1%
 
2.332041e-061< 0.1%
 
2.762065e-061< 0.1%
 
4.381552e-051< 0.1%
 
2.446297e-061< 0.1%
 
Other values (4990)499099.8%
 
ValueCountFrequency (%) 
1.90563e-071< 0.1%
 
1.907716e-071< 0.1%
 
2.174086e-071< 0.1%
 
2.497968e-071< 0.1%
 
2.501578e-071< 0.1%
 
ValueCountFrequency (%) 
0.0005677971< 0.1%
 
0.00056719531< 0.1%
 
0.00056397931< 0.1%
 
0.00056215771< 0.1%
 
0.00056204561< 0.1%
 

nSi(s_reac)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct5000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4499491376
Minimum0.1995639
Maximum0.6999001
Zeros0
Zeros (%)0.0%
Memory size39.1 KiB
2022-11-01T10:15:31.493305image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.1995639
5-th percentile0.225023995
Q10.325030925
median0.44995835
Q30.574935425
95-th percentile0.674862215
Maximum0.6999001
Range0.5003362
Interquartile range (IQR)0.2499045

Descriptive statistics

Standard deviation0.1443316385
Coefficient of variation (CV)0.3207732307
Kurtosis-1.200020607
Mean0.4499491376
Median Absolute Deviation (MAD)0.12501135
Skewness1.596846978e-05
Sum2249.745688
Variance0.02083162188
MonotocityNot monotonic
2022-11-01T10:15:31.821433image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.30407991< 0.1%
 
0.64984391< 0.1%
 
0.44334761< 0.1%
 
0.42144171< 0.1%
 
0.49122741< 0.1%
 
0.53901991< 0.1%
 
0.29503041< 0.1%
 
0.67315141< 0.1%
 
0.35345751< 0.1%
 
0.68737461< 0.1%
 
Other values (4990)499099.8%
 
ValueCountFrequency (%) 
0.19956391< 0.1%
 
0.2001821< 0.1%
 
0.20022231< 0.1%
 
0.20038411< 0.1%
 
0.20041981< 0.1%
 
ValueCountFrequency (%) 
0.69990011< 0.1%
 
0.69972671< 0.1%
 
0.6995261< 0.1%
 
0.69947531< 0.1%
 
0.69945361< 0.1%
 

nPortlandite
Real number (ℝ≥0)

ZEROS

Distinct3129
Distinct (%)62.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3504308244
Minimum0
Maximum1.449003
Zeros1872
Zeros (%)37.4%
Memory size39.1 KiB
2022-11-01T10:15:32.137404image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.2237832
Q30.647392675
95-th percentile1.09328315
Maximum1.449003
Range1.449003
Interquartile range (IQR)0.647392675

Descriptive statistics

Standard deviation0.3848474338
Coefficient of variation (CV)1.098212277
Kurtosis-0.5636870133
Mean0.3504308244
Median Absolute Deviation (MAD)0.2237832
Skewness0.7859318013
Sum1752.154122
Variance0.1481075473
MonotocityNot monotonic
2022-11-01T10:15:32.446473image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0187237.4%
 
0.01238251< 0.1%
 
1.0845581< 0.1%
 
0.27605491< 0.1%
 
0.24739011< 0.1%
 
0.055592911< 0.1%
 
0.66164821< 0.1%
 
0.46334331< 0.1%
 
0.19922331< 0.1%
 
1.1684431< 0.1%
 
Other values (3119)311962.4%
 
ValueCountFrequency (%) 
0187237.4%
 
1.484875e-051< 0.1%
 
0.00021552191< 0.1%
 
0.00022806011< 0.1%
 
0.00054626021< 0.1%
 
ValueCountFrequency (%) 
1.4490031< 0.1%
 
1.4269911< 0.1%
 
1.4264441< 0.1%
 
1.424511< 0.1%
 
1.4204691< 0.1%
 

nAmor-Sl
Real number (ℝ≥0)

ZEROS

Distinct587
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02172151486
Minimum0
Maximum0.5185087
Zeros4414
Zeros (%)88.3%
Memory size39.1 KiB
2022-11-01T10:15:32.805842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.1936624
Maximum0.5185087
Range0.5185087
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.07343233555
Coefficient of variation (CV)3.380626813
Kurtosis15.41392607
Mean0.02172151486
Median Absolute Deviation (MAD)0
Skewness3.886794166
Sum108.6075743
Variance0.005392307904
MonotocityNot monotonic
2022-11-01T10:15:33.165538image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0441488.3%
 
0.1408511< 0.1%
 
0.18466361< 0.1%
 
0.2077641< 0.1%
 
0.5103021< 0.1%
 
0.42290441< 0.1%
 
0.037226881< 0.1%
 
0.16987041< 0.1%
 
0.29237911< 0.1%
 
0.13018721< 0.1%
 
Other values (577)57711.5%
 
ValueCountFrequency (%) 
0441488.3%
 
3.219659e-081< 0.1%
 
0.001753831< 0.1%
 
0.0031708531< 0.1%
 
0.0039177621< 0.1%
 
ValueCountFrequency (%) 
0.51850871< 0.1%
 
0.5103021< 0.1%
 
0.49676971< 0.1%
 
0.49492561< 0.1%
 
0.48905641< 0.1%
 

mCSHQ
Real number (ℝ≥0)

HIGH CORRELATION

Distinct4999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07901833647
Minimum0.01858583
Maximum0.1422003
Zeros0
Zeros (%)0.0%
Memory size39.1 KiB
2022-11-01T10:15:33.478045image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.01858583
5-th percentile0.0335890675
Q10.05451588
median0.076980125
Q30.10223545
95-th percentile0.131111605
Maximum0.1422003
Range0.12361447
Interquartile range (IQR)0.04771957

Descriptive statistics

Standard deviation0.0299134072
Coefficient of variation (CV)0.3785628569
Kurtosis-0.8687011554
Mean0.07901833647
Median Absolute Deviation (MAD)0.02348445
Skewness0.1755894464
Sum395.0916824
Variance0.0008948119304
MonotocityNot monotonic
2022-11-01T10:15:33.821795image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.10560992< 0.1%
 
0.061780621< 0.1%
 
0.04894341< 0.1%
 
0.066203241< 0.1%
 
0.08562531< 0.1%
 
0.099803821< 0.1%
 
0.051207631< 0.1%
 
0.059942011< 0.1%
 
0.13676581< 0.1%
 
0.044773631< 0.1%
 
Other values (4989)498999.8%
 
ValueCountFrequency (%) 
0.018585831< 0.1%
 
0.018632181< 0.1%
 
0.018685651< 0.1%
 
0.018750181< 0.1%
 
0.018820681< 0.1%
 
ValueCountFrequency (%) 
0.14220031< 0.1%
 
0.14216511< 0.1%
 
0.14212431< 0.1%
 
0.14201521< 0.1%
 
0.141971< 0.1%
 

nCa(CSHQ)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct4999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5985703968
Minimum0.1001453
Maximum1.138752
Zeros0
Zeros (%)0.0%
Memory size39.1 KiB
2022-11-01T10:15:34.165021image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.1001453
5-th percentile0.185002265
Q10.40001185
median0.5792055
Q30.792780325
95-th percentile1.0484764
Maximum1.138752
Range1.0386067
Interquartile range (IQR)0.392768475

Descriptive statistics

Standard deviation0.2566197023
Coefficient of variation (CV)0.4287210054
Kurtosis-0.8193980319
Mean0.5985703968
Median Absolute Deviation (MAD)0.1927739
Skewness0.1721072064
Sum2992.851984
Variance0.06585367162
MonotocityNot monotonic
2022-11-01T10:15:34.508772image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.71778012< 0.1%
 
0.49474421< 0.1%
 
0.43784631< 0.1%
 
0.68973681< 0.1%
 
0.42584181< 0.1%
 
0.68569431< 0.1%
 
0.7992371< 0.1%
 
0.27592011< 0.1%
 
0.48002051< 0.1%
 
1.0952311< 0.1%
 
Other values (4989)498999.8%
 
ValueCountFrequency (%) 
0.10014531< 0.1%
 
0.1003951< 0.1%
 
0.10068321< 0.1%
 
0.10103091< 0.1%
 
0.10141081< 0.1%
 
ValueCountFrequency (%) 
1.1387521< 0.1%
 
1.138471< 0.1%
 
1.1381431< 0.1%
 
1.1372691< 0.1%
 
1.1369071< 0.1%
 

nSi(CSHQ)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct4999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4282276226
Minimum0.148386
Maximum0.6999001
Zeros0
Zeros (%)0.0%
Memory size39.1 KiB
2022-11-01T10:15:34.821277image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.148386
5-th percentile0.21274362
Q10.30032245
median0.42105675
Q30.552095675
95-th percentile0.669445875
Maximum0.6999001
Range0.5515141
Interquartile range (IQR)0.251773225

Descriptive statistics

Standard deviation0.1470406083
Coefficient of variation (CV)0.3433702092
Kurtosis-1.153331457
Mean0.4282276226
Median Absolute Deviation (MAD)0.1254675
Skewness0.1182319873
Sum2141.138113
Variance0.0216209405
MonotocityNot monotonic
2022-11-01T10:15:35.166265image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.23470422< 0.1%
 
0.30407991< 0.1%
 
0.67315141< 0.1%
 
0.42392631< 0.1%
 
0.44334761< 0.1%
 
0.42144171< 0.1%
 
0.49122741< 0.1%
 
0.40883271< 0.1%
 
0.29503041< 0.1%
 
0.35345751< 0.1%
 
Other values (4989)498999.8%
 
ValueCountFrequency (%) 
0.1483861< 0.1%
 
0.14875611< 0.1%
 
0.1491831< 0.1%
 
0.14969821< 0.1%
 
0.1502611< 0.1%
 
ValueCountFrequency (%) 
0.69990011< 0.1%
 
0.69972671< 0.1%
 
0.6995261< 0.1%
 
0.69945361< 0.1%
 
0.699261< 0.1%
 

nH2O(CSHQ)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct4997
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.094753231
Minimum0.225046
Maximum2.014349
Zeros0
Zeros (%)0.0%
Memory size39.1 KiB
2022-11-01T10:15:35.478763image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.225046
5-th percentile0.41354726
Q10.748320975
median1.063101
Q31.42460475
95-th percentile1.8562683
Maximum2.014349
Range1.789303
Interquartile range (IQR)0.676283775

Descriptive statistics

Standard deviation0.4347075134
Coefficient of variation (CV)0.3970826493
Kurtosis-0.8332727123
Mean1.094753231
Median Absolute Deviation (MAD)0.337325
Skewness0.1769507841
Sum5473.766157
Variance0.1889706222
MonotocityNot monotonic
2022-11-01T10:15:35.822526image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.0340952< 0.1%
 
1.5105882< 0.1%
 
1.1060152< 0.1%
 
0.84911271< 0.1%
 
1.3048061< 0.1%
 
1.2200821< 0.1%
 
0.87064411< 0.1%
 
1.2129311< 0.1%
 
1.4137781< 0.1%
 
0.62004611< 0.1%
 
Other values (4987)498799.7%
 
ValueCountFrequency (%) 
0.2250461< 0.1%
 
0.22560721< 0.1%
 
0.22625471< 0.1%
 
0.2270361< 0.1%
 
0.22788971< 0.1%
 
ValueCountFrequency (%) 
2.0143491< 0.1%
 
2.013851< 0.1%
 
2.0132721< 0.1%
 
2.0117261< 0.1%
 
2.0110851< 0.1%
 

C/S(CSHQ)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1298
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.391996242
Minimum0.6737822
Maximum1.627022
Zeros0
Zeros (%)0.0%
Memory size39.1 KiB
2022-11-01T10:15:36.227933image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.6737822
5-th percentile0.6748973
Q11.15805375
median1.627021
Q31.627021
95-th percentile1.627021
Maximum1.627022
Range0.9532398
Interquartile range (IQR)0.46896725

Descriptive statistics

Standard deviation0.3581761674
Coefficient of variation (CV)0.2573111598
Kurtosis-0.409633022
Mean1.391996242
Median Absolute Deviation (MAD)0
Skewness-1.133213753
Sum6959.98121
Variance0.1282901669
MonotocityNot monotonic
2022-11-01T10:15:36.587310image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.627021274554.9%
 
1.627023777.5%
 
0.67489733567.1%
 
0.67489721663.3%
 
0.6748974450.9%
 
0.6748971160.3%
 
0.67489752< 0.1%
 
1.5664332< 0.1%
 
1.010762< 0.1%
 
1.0449671< 0.1%
 
Other values (1288)128825.8%
 
ValueCountFrequency (%) 
0.67378221< 0.1%
 
0.6748971160.3%
 
0.67489721663.3%
 
0.67489733567.1%
 
0.6748974450.9%
 
ValueCountFrequency (%) 
1.6270221< 0.1%
 
1.627021274554.9%
 
1.627023777.5%
 
1.6270191< 0.1%
 
1.6270111< 0.1%
 

nGelPW(CSH)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct4999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4513118892
Minimum0.07688994
Maximum0.8614756
Zeros0
Zeros (%)0.0%
Memory size39.1 KiB
2022-11-01T10:15:36.931077image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.07688994
5-th percentile0.14287826
Q10.303305
median0.4362231
Q30.595593475
95-th percentile0.793085535
Maximum0.8614756
Range0.78458566
Interquartile range (IQR)0.292288475

Descriptive statistics

Standard deviation0.1928144846
Coefficient of variation (CV)0.4272311215
Kurtosis-0.787328097
Mean0.4513118892
Median Absolute Deviation (MAD)0.14275475
Skewness0.1956199976
Sum2256.559446
Variance0.03717742547
MonotocityNot monotonic
2022-11-01T10:15:37.290595image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.46679132< 0.1%
 
0.37427831< 0.1%
 
0.81497571< 0.1%
 
0.51873371< 0.1%
 
0.60462971< 0.1%
 
0.21184681< 0.1%
 
0.36313961< 0.1%
 
0.82855191< 0.1%
 
0.23168331< 0.1%
 
0.18522931< 0.1%
 
Other values (4989)498999.8%
 
ValueCountFrequency (%) 
0.076889941< 0.1%
 
0.077081631< 0.1%
 
0.077302881< 0.1%
 
0.077569831< 0.1%
 
0.077861491< 0.1%
 
ValueCountFrequency (%) 
0.86147561< 0.1%
 
0.86126221< 0.1%
 
0.86101511< 0.1%
 
0.86035391< 0.1%
 
0.86007991< 0.1%
 

Interactions

2022-11-01T10:14:01.633669image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:02.022364image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:02.323527image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:02.573531image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:02.854785image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:03.131626image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:03.387147image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:03.683699image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:03.964925image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:04.245412image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:04.511038image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:04.776688image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:05.042305image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:05.323652image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:05.589289image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:05.870539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:06.138821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:06.417569image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:06.698824image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:06.948821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:07.229360image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:07.510609image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:07.760622image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:08.026223image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:08.557665image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:08.823295image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:09.074756image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:09.323772image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:09.573781image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:09.855033image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:10.127035image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:10.402243image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:10.707035image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:10.957040image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:11.230092image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:11.480082image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:11.730100image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:11.964480image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:12.214471image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:12.464474image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:12.714482image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:12.972609image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:13.245733image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:13.511374image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:13.776987image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:14.011368image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:14.278210image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:14.512594image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:14.746950image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:15.012588image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:15.261982image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:15.527618image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:15.808872image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:16.083618image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:16.341256image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:16.622502image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:16.888149image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:17.168517image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:17.434146image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:17.699772image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:18.292678image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:18.573915image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:18.839571image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:19.114255image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:19.371104image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:19.621097image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:19.917978image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:20.184604image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:20.450241image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:20.748102image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:20.998110image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:21.262764image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:21.544029image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:21.778392image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:22.028390image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:22.279552image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:22.529552image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:22.779571image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:23.059987image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:23.295350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:23.545338image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:23.779706image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:24.014090image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:24.294457image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:24.524930image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:24.790557image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:25.040568image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:14:25.326502image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2022-11-01T10:15:21.926981image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:15:22.223554image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:15:22.504801image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:15:22.786070image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2022-11-01T10:15:37.696851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-01T10:15:38.322050image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-01T10:15:38.900182image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-01T10:15:39.477503image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-01T10:15:23.456658image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-11-01T10:15:24.253051image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Sample

First rows

T(C)b(CaO)b(SiO2)b(H2O)Vol(aq)pHnCa(aq)nCa(s)nSi(aq)nSi(s_reac)nPortlanditenAmor-SlmCSHQnCa(CSHQ)nSi(CSHQ)nH2O(CSHQ)C/S(CSHQ)nGelPW(CSH)
025.00.5080600.3040813.4388760.04606112.4726100.0009340.5071271.424097e-060.3040800.0123820.0000000.0617810.4947440.3040800.8751581.6270210.374278
125.01.2587910.5665484.6557740.04855012.4726100.0009841.2578071.501005e-060.5665460.3360250.0000000.1151070.9217820.5665461.6305501.6270210.697336
225.01.3965910.6971164.6716130.04339812.4726100.0008801.3957111.341699e-060.6971150.2614910.0000000.1416341.1342210.6971152.0063331.6270210.858048
325.01.4614230.3276797.3285840.09856012.4726100.0019981.4594253.047140e-060.3276760.9262900.0000000.0665750.5331350.3276760.9430681.6270210.403321
425.00.6679070.3899257.3429610.11174512.4726100.0022650.6656423.454804e-060.3899220.0312310.0000000.0792210.6344110.3899221.1222161.6270210.479938
525.01.5007650.2815332.8502940.01801212.4726100.0003651.5004005.568708e-070.2815321.0423410.0000000.0572000.4580590.2815320.8102641.6270200.346525
625.01.7903230.4953065.9194030.06338212.4726100.0012851.7890381.959534e-060.4953040.9831680.0000000.1006320.8058700.4953041.4255111.6270200.609648
725.00.1972480.5082643.3058560.0517259.7936550.0000540.1971942.038197e-040.5080600.0000000.2158760.0365970.1971940.2921840.4431330.6748970.151402
825.01.2819220.6867012.9837930.01522112.4726100.0003091.2816134.705838e-070.6867010.1643380.0000000.1395191.1172760.6867011.9763601.6270200.845229
925.01.5846840.5321107.2941030.09109212.4726100.0018461.5828382.816162e-060.5321070.7170890.0000000.1081090.8657490.5321071.5314311.6270210.654946

Last rows

T(C)b(CaO)b(SiO2)b(H2O)Vol(aq)pHnCa(aq)nCa(s)nSi(aq)nSi(s_reac)nPortlanditenAmor-SlmCSHQnCa(CSHQ)nSi(CSHQ)nH2O(CSHQ)C/S(CSHQ)nGelPW(CSH)
499025.01.7960560.5239224.6845250.04033012.472610.0008171.7952391.246846e-060.5239210.9428090.00.1064460.8524290.5239211.5078711.6270210.644870
499125.00.2785950.3109597.2931640.12126211.425630.0002100.2783855.025417e-050.3109080.0000000.00.0447670.2783850.3109080.5814500.8953930.215409
499225.00.4717660.3702353.3308870.04419912.021670.0002840.4714825.358847e-060.3702300.0000000.00.0646100.4714820.3702300.8839941.2734840.338774
499325.01.5056710.6120416.4055180.07466512.472610.0015131.5041582.308359e-060.6120380.5083590.00.1243490.9957990.6120381.7614781.6270210.753331
499425.00.7475710.6754354.2184000.04978511.763360.0001740.7473971.059425e-050.6754250.0000000.00.1088440.7473970.6754251.4626091.1065590.539592
499525.01.1732910.2652352.9439640.02598612.472610.0005271.1727648.034066e-070.2652340.7412230.00.0538880.4315410.2652340.7633571.6270210.326465
499625.01.1284750.4113297.9254390.11346312.472610.0023001.1261753.507885e-060.4113250.4569410.00.0835700.6692340.4113251.1838151.6270210.506282
499725.01.6016540.5377477.4247360.09301812.472610.0018851.5997692.875722e-060.5377440.7248490.00.1092550.8749200.5377441.5476541.6270210.661885
499825.01.6559620.5526597.3475020.09030512.472610.0018301.6541322.791928e-060.5526560.7549490.00.1122840.8991830.5526561.5905731.6270210.680240
499925.00.4369100.4157022.9004610.03670711.680020.0001060.4368049.246345e-060.4156920.0000000.00.0651200.4368040.4156920.8686331.0507870.319508